The mixing process in biogas plants is fundamental for effectiveness. Unfortunately, difficulties at full scale mean that research must usually be performed at lab‐scale and the results up‐scaled. This study presents a scale‐up strategy for the fluid flow in shear‐thinning non‐Newtonian fluids. Dimensional analysis was applied to connect the influencing parameters, i.e., geometric dimensions, rotational speed, and rheological properties, to key figures. By using the Reynolds and Hedström number, standards for adapting the rotational speed and rheological properties were defined as a function of the scale. A sliding‐mesh model was selected for numerical simulations of a plant with a paddle agitator at two different scales. The scale‐up approach leads to similar velocity fields, which result in equal normalized mixing times.

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Die Auslegung und Optimierung von Rührsystemen in Biogasanlagen erfolgt nach wie vor auf Basis empirischer Erfahrungswerte. Mithilfe der particle image velocimetry (PIV) wurde das Strömungsfeld eines Paddelrührwerks unter dem Einfluss der Einbaulage und der Fluidviskosität im Labormaßstab untersucht. Dabei wurden die Viskositätseigenschaften von Fermentersuspensionen mithilfe eines Rohrviskosimeters bestimmt. Über Maßstabsgesetze konnten die Viskositätseigenschaften der Modellsuspensionen für den Versuch angepasst werden. Als Ergebnis zeigt sich, dass eine geänderte Einbaulage Totzonen um bis zu 55 % reduzieren kann.

For acoustic procedures which rely on the speed of sound to derive process parameters, the determination of the acoustic time of flight is essential. In this work, a method for the determination of the time of flight (TOF) is presented. It is intended for reverberant and noisy environments and can be applied in the gas holdup determination in bubbly liquids via acoustic transmission tomography (GHATT) for example. The method includes the selection and design of the transmitted signal to optimize the disambiguate of the autocorrelation, the narrowing of the time window based on the Fractional Fourier Transform (FrFT) to accelerate the TOF estimation. Furthermore, it includes the consideration of the system-induced signal distortion through prior quasi-anechoic measurements and the sparse reconstruction of the spatial impulse response for TOF estimation using non-negative sparse deconvolution algorithms. The method is tested analytically on numerically generated signals and various sparse deconvolution algorithms are investigated with respect to their applicability and limitations.